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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Cross-Sectional Research01:50

Cross-Sectional Research

In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
Introduction to Developmental Psychology01:27

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Developmental psychology explores the changes and continuities in human abilities throughout life, encompassing physical, cognitive, linguistic, and social dimensions. Human development is not restricted to growth, but includes aspects of decline, particularly in physical abilities as individuals age. Developmental psychologists seek to understand how people change as they age and how their mental and social skills evolve.Developmental MilestonesA key concept in developmental psychology is...
Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

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Related Experiment Video

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

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Published on: September 17, 2019

Classifying developmental trajectories over time should be done with great caution: a comparison between methods.

Jos Twisk1, Trynke Hoekstra

  • 1Department of Methodology and Applied Biostatistics, Institute of Health Sciences, Faculty of Earth and Life Science, Vrije Universiteit, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands. jos.twisk@falw.vu.nl

Journal of Clinical Epidemiology
|July 24, 2012
PubMed
Summary
This summary is machine-generated.

This study compared five statistical methods for analyzing developmental trajectories in epidemiological data. Latent Class Growth Analysis (LCGA) best identified linear trajectories, but caution is advised for all methods.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal cohort studies increasingly focus on developmental trajectories within subpopulations.
  • Advanced statistical methods for trajectory analysis are underutilized in epidemiology.
  • A need exists to evaluate methods for detecting developmental trajectories.

Purpose of the Study:

  • To compare the performance of five statistical methods in detecting developmental trajectories.
  • To assess methods using real-life and manipulated longitudinal epidemiological data.

Main Methods:

  • Compared K-means clustering, two-step modeling, Latent Class Analysis (LCA), Latent Class Growth Analysis (LCGA), and Latent Class Growth Mixture Modeling (LCGMM).
  • Applied methods to a real-life dataset and two manipulated datasets with linear and quadratic developments.

Main Results:

  • All five methods yielded comparable trajectories for the real-life data.
  • LCGA demonstrated superior performance in identifying linear trajectories.
  • No method effectively detected combined linear and quadratic trajectories; LCA and LCGA suggested more classes than LCGMM.

Conclusions:

  • Latent Class Growth Analysis (LCGA) and Latent Class Growth Mixture Modeling (LCGMM) show promise for trajectory analysis.
  • All compared classification methods require careful application due to performance variations.
  • Further research is needed to refine methods for complex developmental patterns.